Update app.py

app.py

@@ -1,19 +1,22 @@
-# Stable Diffusion Hugging Face App (Gradio UI + Style Selection + Custom Loss Placeholder)
+# Stable Diffusion Hugging Face App (Turbo Version for Fast Startup)
 
 import gradio as gr
 import torch
 from diffusers import StableDiffusionPipeline, DDIMScheduler
 from transformers import CLIPTextModel, CLIPTokenizer
 
-# Load pre-trained models
+# Load the lightweight Stable Diffusion Turbo model
+model_id = "stabilityai/sd-turbo"
 device = "cuda" if torch.cuda.is_available() else "cpu"
+
 pipe = StableDiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
+    model_id,
     torch_dtype=torch.float16 if device == "cuda" else torch.float32,
 ).to(device)
+
 pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
 
-# Example styles from textual inversion (simulated via prompts)
+# Simulated style prompts (not using learned embeddings)
 STYLE_MAP = {
     "Van Gogh": "in the style of Van Gogh",
     "Cyberpunk": "cyberpunk futuristic cityscape",
@@ -22,15 +25,15 @@ STYLE_MAP = {
     "Surrealism": "in surrealistic dreamscape style"
 }
 
-# Custom loss placeholder (not applied at inference, for academic purposes)
+# Custom loss placeholder (for assignment purposes)
 def custom_loss_placeholder(image_tensor):
-    # Example: "yellow_loss" = penalize lack of yellow pixels
     yellow = torch.tensor([1.0, 1.0, 0.0]).to(image_tensor.device)
-    image_mean = image_tensor.mean(dim=[1, 2])  # Average over H and W
+    image_mean = image_tensor.mean(dim=[1, 2])
     yellow_loss = torch.nn.functional.mse_loss(image_mean, yellow)
     return yellow_loss
 
 # Generate image based on prompt and style
+
 def generate(prompt, style, seed):
     torch.manual_seed(seed)
     full_prompt = f"{prompt}, {STYLE_MAP.get(style, '')}"
@@ -38,18 +41,20 @@ def generate(prompt, style, seed):
     return result
 
 # Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("""# Stable Diffusion Style Generator\nGenerate styled images using Stable Diffusion + Textual Inversion Styles.""")
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown("""# Stable Diffusion Turbo App\nGenerate styled images using text prompts and different art styles.""")
 
     with gr.Row():
-        prompt = gr.Textbox(label="Enter Prompt", placeholder="A cat riding a bicycle through space")
+        prompt = gr.Textbox(label="Enter Prompt", placeholder="A fox with a monocle")
         style = gr.Dropdown(choices=list(STYLE_MAP.keys()), label="Choose Style", value="Van Gogh")
         seed = gr.Slider(minimum=0, maximum=9999, step=1, value=42, label="Random Seed")
 
-    btn = gr.Button("Generate Image")
+    generate_btn = gr.Button("Generate Image")
     output = gr.Image(label="Stylized Output")
 
-    btn.click(fn=generate, inputs=[prompt, style, seed], outputs=output)
+    generate_btn.click(fn=generate, inputs=[prompt, style, seed], outputs=output)
 
-# Launch app
+# Launch the Gradio app
 demo.launch()